Borui Wang

Hi, I am a Master student researcher in the Computer Science Department of Stanford University. I work in machine learning, computer vision and robotics at the Stanford Artificial Intelligence Laboratory.

My research interests are in deep learning, reinforcement learning, probabilistic graphical models, Bayesian nonparametrics and deep generative models, with applications in computer vision, robotics and natural language processing.

Before coming to Stanford, I spent four wonderful undergraduate years at Harvard University and received my Bachelor of Arts degree in Computer Science with a secondary field in Economics from Harvard.

I have also worked in the Machine Learning Department of Carnegie Mellon University for one year conducting research on machine learning theories. At CMU I was very fortunate to work with Professor Geoff Gordon to design and study new learning algorithms for general latent-variable graphical models.

Google Scholar

• [New]   2019.11.10 - Our paper “Learning General Latent-Variable Graphical Models with Predictive Belief Propagation” is accepted to AAAI 2020.
• 2019.07.22 - Our paper “Imitation Learning for Human Pose Prediction” is accepted to ICCV 2019.
• 2018.11.05 - Our paper “Action-Agnostic Human Pose Forecasting” is accepted to WACV 2019.

We introduce a novel formulation of message-passing inference over junction trees named predictive belief propagation, and propose a new learning and inference algorithm for general latent-variable graphical models based on this formulation. Our proposed algorithm reduces the hard parameter learning problem into a sequence of supervised learning problems, and unifies the learning of different kinds of latent graphical models into a single learning framework that is local-optima-free and statistically consistent.

We propose a new reinforcement learning formulation for the problem of human pose prediction in videos, and develop an imitation learning algorithm for predicting future poses under this formulation through a combination of behavioral cloning and generative adversarial imitation learning.

We propose a new action-agnostic method for both short-term and long-term human pose forecasting. To this end, we propose a new recurrent neural network for modeling the hierarchical and multi-scale characteristics of the human motion dynamics, denoted by triangular-prism RNN (TP-RNN). Our model captures the latent hierarchical structure embedded in temporal human pose sequences by encoding the temporal dependencies with different time-scales.

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